Systems and Methods for Monitoring and Controlling a Multi-Phase Fluid Flow

ABSTRACT

Systems and methods for monitoring and controlling dynamic multi-phase flow phenomena, capable of sensing, detecting, quantifying, and inferring characteristics, properties, and compositions (including static and dynamic characteristics, properties and compositions). The systems combine machine vision and mathematical models, which enables direct observation and detection of static and dynamic multi-phase fluid flow properties and phenomena (e.g. voids, waves, shadows, dimples, wrinkles, foam, bubbles, particulates, discrete materials, collections of materials, and position) and inferring other properties and phenomena (e.g. flow regimes, bubble velocities and accelerations, material deposition rates, erosion rates, phasic critical behavioral points as related to heat transfer, and the volumetric and mass flow rates of the phases) that are used to monitor and control systems applied to a multi-phase fluid flow system.

CROSS-REFERENCE TO RELATED APPLICATIONS

The priority of U.S. Provisional Application No. 62/827,187, filed Apr.1, 2019, U.S. Provisional Application No. 62/863,649, filed Jun. 19,2019, U.S. Provisional Application No. 62/900,469, filed Sep. 14, 2019,and U.S. Provisional Application No. 62/936,948, filed Nov. 18, 2019, ishereby claimed and the specifications thereof are incorporated herein byreference.

FIELD OF THE DISCLOSURE

The present disclosure pertains to systems and methods for monitoringand controlling a multi-phase fluid flow that is capable of sensing,detecting, quantifying, and inferring characteristics, properties, andcompositions, including static and dynamic characteristics, propertiesand compositions of the multi-phase fluid flow. More particularly, thesystems combine machine vision and mathematical models, which enabledirect observation and detection of static and dynamic multi-phase fluidflow properties and phenomena (e.g. voids, waves, shadows, dimples,wrinkles, foam, bubbles, particulates, discrete materials, collectionsof materials, and position) and inferring other properties and phenomena(e.g. flow regimes, bubble velocities and accelerations, materialdeposition rates, erosion rates, phasic critical behavioral points asrelated to heat transfer, and the volumetric and mass flow rates of thephases) that may be used to monitor and control systems requiring amulti-phase fluid flow.

BACKGROUND

Virtually all processing technologies involve some form of multiphasefluid flow. In fluid mechanics, a multi-phase flow is the simultaneousflow of materials with two or more thermodynamic phases. Multi-phasefluids therefore, include any combination of liquids and/or gases, whichmay be transmitted with compositions of particulates, muds, rocks,debris, organic materials, inorganic materials, crystals, molecules,atoms, ions, electrons, and any material that can flow in a path orchannel, or flow over a plate or object, or flow through space. Asdescribed herein, flow generally refers to any transport of materialscollectively or fluidly, from one point to another.

Multi-phase fluid flow is inherently unstable. In a two-phase flow, forexample, several flow regimes can exist (e.g. bubbly flow, slug flow,annular flow) and are highly sensitive to the external accelerationfield. In thermal management applications for space, where weakgravitational forces are unable to effectively drive phase separation,the inherent instabilities are exacerbated causing serious designproblems and presenting high operational risks due to the potentiallysizeable thermal transients that can occur as a function of flow regimeand the acceleration field. Further, flow regime, flow rate, heat-flux,flow quality, void fraction, phase velocities, pressure drop, pressure,and temperature are all interdependent in a two-phase flow where achange in the flow regime can cause step changes in the other fluiddynamic and thermodynamic properties of the flow. However, attributessuch as void fraction and phasic fluid flow rates and velocities havebeen traditionally unaccommodating to real-time measurement. Due to theinstability inherent in a multi-phase fluid flow, a need exists toaccurately discern the thermal and fluid characteristics in amulti-phase fluid flow channel and provide such information in real-timeto a control system.

Techniques have been developed to mitigate the problem of observingintrinsic properties of a multi-phase fluid flow in real-time byinferring, for example, the void fraction in a two-phase flow as afunction of the capacitance across a flow channel, and the flow regime.Such techniques can be useful for special cases of a two-phase flow buthave severe inherent scope limitations since their calibrations tend todrift, and they primarily apply to small channel flows and fluids with aconstant and high dielectric behavior. These techniques thus, canexclude large classes of two-phase fluid flow applications (e.g. largechannels, low and varying dielectrics, electrical interference) , aswell as most multi-phase fluid flow applications.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described below with references to theaccompanying drawings in which like elements are referenced with likereference numerals, and in which:

FIG. 1 is a schematic diagram illustrating an intelligent sensor system.

FIG. 2 is a schematic diagram illustrating an exemplary closed loopprocess for monitoring and controlling multi-phase fluid flow phenomenawith the intelligent sensor system in FIG. 1.

FIG. 3 is a schematic diagram illustrating an exemplary open loopprocess for monitoring and controlling multi-phase fluid flow phenomenawith the intelligent sensor system in FIG. 1.

FIG. 4 is a schematic diagram illustrating an exemplary open loopprocess for monitoring and controlling multi-phase fluid flow in theform of exhaust from a rocket nozzle with the intelligent sensor systemin FIG. 1.

FIG. 5 is a schematic diagram illustrating exemplary post-processorinputs and outputs for the intelligent sensor system in FIG. 1.

FIG. 6 is a schematic diagram illustrating various quantities detectedfor inferring geometry, position and dynamic attributes of a multi-phasefluid flow.

DETAILED DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENTS

The subject matter of the present disclosure is described withspecificity, however, the description itself is not intended to limitthe scope of the disclosure. The subject matter thus, might also beembodied in other ways, to include different structures, steps and/orcombinations similar to and/or fewer than those described herein, inconjunction with other present or future technologies. Although the term“step” may be used herein to describe different elements of methodsemployed, the term should not be interpreted as implying any particularorder among or between various steps herein disclosed unless otherwiseexpressly limited by the description to a particular order. Otherfeatures and advantages of the disclosed embodiments will be or willbecome apparent to one of ordinary skill in the art upon examination ofthe following figures and detailed description. It is intended that allsuch additional features and advantages be included within the scope ofthe disclosed embodiments. Further, the illustrated figures are onlyexemplary and are not intended to assert or imply any limitation withregard to the environment, architecture, design, or process in whichdifferent embodiments may be implemented.

In one embodiment, the present disclosure includes a system formonitoring a multi-phase fluid flow, comprising: i) a preprocessor forreceiving, filtering, and formatting digital images of the multi-phasefluid flow; ii) a processor that includes a neural network learningalgorithm linked to the preprocessor for receiving the preprocesseddigital images, identifying phenomena related to the multi-phase fluidflow in the preprocessed digital images and quantifying the relativepositions and geometries of the phenomena based on the digital images;and iii) a post processor linked to the processor for receiving theidentified phenomena, receiving the quantified relative positions andgeometries of the phenomena, deriving dynamic attributes of theidentified phenomena and inferring other attributes related to themulti-phase fluid flow.

In another embodiment, the present disclosure includes a method formonitoring a multi-phase fluid flow, comprising: i) capturing at leastone attribute of the multi-phase fluid flow; ii) identifying phenomenarelated to the multi-phase fluid flow based on the at least oneattribute of the multi-phase fluid flow; iii) quantifying the relativepositions and geometries of the phenomena; iv) deriving dynamicattributes related to the phenomena by correlating relative positionsand geometries of the phenomena based on a sequence of the at least oneattribute captured over a predetermined time-period; and v) inferringother attributes related to the multi-phase fluid flow based on theidentified phenomena, the quantified relative positions and geometriesof the phenomena and the derived dynamic attributes related to thephenomena.

The present disclosure overcomes one or more deficiencies in the priorart by providing systems and methods for monitoring and controllingdynamic multi-phase flow phenomena using machine vision and mathematicalmodels, which enable direct observation and detection of static anddynamic multi-phase fluid flow properties and phenomena (e.g. voids,waves, shadows, dimples, wrinkles, foam, bubbles, particulates, discretematerials, collections of materials, and position) and inferring otherproperties and phenomena (e.g. bubble velocities and accelerations,material deposition rates, erosion rates, phasic critical behaviorpoints as related to heat transfer, and the volumetric and mass flowrates of the phases). The ability to sense, detect, quantify, and infercharacteristics, properties and compositions, including static anddynamic characteristics, properties and compositions, sets the systemand methods herein apart from conventional processes.

Referring now to FIG. 1, a schematic diagram illustrates an intelligentsensor system 100, that directly identifies and quantifies flow regimesand other multi-phase fluid flow phenomena in multi-phase fluid flows,in real-time, or as needed. The sensor system 100 is composed of acamera 102, a preprocessor 104, a processor 106, and a post processor108. Information in the form of digital images are created of the source(multi-phase fluid flow) by the camera 102 with a frame rate fast enoughto discern the fluid flow phenomena. The camera 102 acquires a sequenceof images (e.g. a video recording) and provides the images as orderedinput to the pre-processor 104 that prepares the data, as required, asdigital information input to the processor 106. The sensor system 100can, in real-time or otherwise, simultaneously identify and localize theflow regimes and other multi-phase fluid flow phenomena and behaviors inthe images from the camera 102. For a wide field of view, the sensorsystem 100 can simultaneously identify and localize the flow regimes andother multi-phase fluid flow phenomena in multiple flow and stagnantchannels, and at several different locations along each channel withinthe field of view of the camera, utilizing one camera 102. Nevertheless,for large or complex regions of interest, a distributed imaging system(e.g. multiple cameras) could be utilized.

The sensor system 100 can be trained (e.g. via neural network learningalgorithms, such as an appropriate convolutional neural network learningalgorithm (“CNN”), recurrent neural network back-propagation algorithm,or genetic algorithm) to recognize and detect select multi-phase fluidflow phenomena prior to its implementation. Such training may includebackpropagation and its variants, and genetic algorithms. The trainingof the processor 106 can be accomplished off-line, online, or acombination of both. Additionally, the sensor system 100 recognitioncomponents may include directly programmed components (e.g. non-learningor non-neural network-based learning) such as image segmentationprocesses.

The CNN identifies and detects, within each digital image, types ofphenomena and behaviors, and the relative position (within the bordersof the image) of such phenomena and behaviors. The purpose of thealgorithms is to extract visually recognizable information from images,where such images may be a sequence of image frames comprising a stored(i.e. previously recorded) video, live camera video, live camera feed,or other composition of images. The outputs of the CNN are selectqualitative and quantitative descriptions of the multi-phase fluid flowfield and associated phenomena and behaviors, which can include theidentification of flow regimes and artifacts (e.g. bubbles, shadows,voids, particulates, particulate dispersions and patterns, mud and filmthicknesses, and waves), and the relative positions and geometries ofsuch flow regimes, phenomena, and artifacts.

A key feature of the sensor system 100 is the use of a deep learningCNN, employing an object detection algorithm, as the processor 106because of its ability to learn and to recognize an essentiallyunlimited variety of visual observable features and behaviors in images.The processor 106 can also be implemented by other object recognitionalgorithms and processes, including those that are incapable of onlinelearning, such as image segmentation algorithms. The processor 106 canalso be extended to recognize nonvisual features, e.g. sound, via otheradaptive learning algorithms, such as deep learning recurrent neuralnetworks, including Long-Short-Term-Memory implementations of suchnetworks. Other types of neural networks can be utilized as theprocessor 106, including, but not limited to, multilayer perceptronnetworks. As part of its recognition capability, specific classes, typesof phenomena, associated geometries and positioning can be inferred bythe CNN and transferred to the post-processor 108 for computing andinferring other attributes, such as, for example, volume, dragcoefficients, mass flow rates, phase velocities, and bubble growthrates.

Another key feature of the sensor system 100 is its ability to couplethe observations of visually inspectable phenomena, artifacts andpositions in a sequence of images with dynamic behaviors inferred froman observed series of positions as extracted from such sequence ofimages. The CNN learning and recognition feature thus, can superimposepolygons around the detected phenomena and artifacts (with verticesassigned from a coordinate system super-imposed on the image pixelfield) to aid the inference of geometry, position and dynamic behaviorsin the flow field as illustrated in FIG. 6. Dynamic behaviors include,but are not limited to velocities, accelerations, vibration frequencies,and integrations (e.g. summations of areas or volumes), which allowsmathematical derivatives and integrations of observed positions to beinferred by the sensor system 100 and enables it to further inferdifferential equations describing the dynamic behaviors of phenomena andartifacts observed in the flow field.

The coupling of observations with derived dynamic behaviors primarilyoccurs in the post-processing of the observations as illustrated in FIG.5. In this manner, the post-processor 108 can utilize physics-basedtheoretical and empirical models for the accurate estimation andfiltering of behaviors and state variables that may be embedded,ephemeral, and otherwise difficult to observe and measure. The result ispowerful, since access to differential equations determining multi-phasefluid flow behaviors can lead to the design of stable and sophisticatedprocesses by engineers and encourage wide-spread adoption of multi-phasefluid flow processes in orbital systems.

Further, the sensor system 100 can be extended to learn and recognizenonvisual features and behaviors by including alternate learning methodsinto the processor 106, including, but not limited to, recurrent neuralnetwork formulations, as required. The purpose of such auxiliary neuralnetworks is to enable the learning and recognition of information thatis auxiliary to the images (e.g. sound) to enable greater informationalcontext into the post-processor 108 for detecting and characterizingflow phenomena.

The outputs of the processor 106 are provided to a post-processor 108that processes such outputs (now as inputs into such post-processor) asrequired to provide appropriately useful inputs to monitoring andcontrol units. The outputs of the post-processor 108 include directlyobserved properties, and computed and inferred properties and dynamicbehaviors, phenomena, and artifacts, (e.g. velocities, accelerations,and vibration frequencies, drag coefficients, refraction indexes,reflections, mixing, and stratifications, thermodynamic quality, phasevelocities, and phase mass flow rates), and operational and theoreticalconclusions. Control units utilize the outputs from the post-processor108 as inputs to inform control algorithms and models concerning thecorrect actions required of active components to accomplish the goals ofthe multi-phase fluid flow system and dependent and governing systems.Such goals may include regulating the performance of heat exchangers,controlling flow rates, mitigating non-desirable states, managingmaterial deposition, managing material composition, optimizing chemicalreactions, or otherwise working with the flow to achieve a physicalgoal.

For dynamic data inference, the post-processor 108 will typically expectthat the images are ordered sequentially in time with a known orderivable time differential between images (e.g. video image frames).However, such ordering and known time differential between images is notnecessary for all applications where the post-processor 108 isprogrammed to interpret the image sequence stochastically or in someother nonlinear fashion that supports the monitoring and controlobjectives of the fluid processes.

Software (or hardware) comprising and supporting the pre-processor 104and the post-processor 108 may also include adaptive learningcomponents, such as neural networks, as well as non-adaptive, relativelyfixed coded, theoretical and empirical models. All implementations ofthe logic associated with the sensor system 100 therefore, may beimplemented partially or fully in software or hardware (e.g.electronics), as technology permits, and is advantageous for reasonsincluding, but not limited to, cost, speed, and form factors.

Referring now to FIGS. 2-4, the schematic diagrams illustrate exemplaryprocesses for monitoring and controlling multi-phase fluid flowphenomena with the sensor system 100 in FIG. 1. The exemplary processesinclude a closed loop process (FIG. 2), an open-loop process (FIG. 3)and an open loop process applied to the exhaust from a rocket nozzle(FIG. 4).

The processor 106 may include a: i) feedforward neural network; ii)recurrent neural network; iii) CNN; iv) radial basis function network;v) combination of i)-iv) or other networks providing similar patternrecognition capability; or vi) empirical networks incorporating selectnon-empirical or first principal models.

The processor 106 may include version 3 of the YOLO (You Only Look Onceby Redmon and company) object detection algorithm, allowing theprocessor 106 to identify all objects in an image in one pass. However,other object detection algorithms can be used (e.g. RetinaNet byFacebook AI Research, the R-CNN algorithm, the Single Shot MultiBoxDefender algorithm, and others) and other algorithms can evolve from theresearch and development at large, to perform faster and produce higherquality results for certain objectives and applications. Such algorithmsmay also utilize image segmentation, multi-lateral bounding-boxes, orother strategies including algorithms external to, or embedded in, a CNNstructure. Further, other learning structures may supplant a CNNconcerning efficiency and performance, and may include greatercapability to learn and apply context and provide greater capability forimplicit or explicit reasoning and therefore, reduce some of the postprocessing requirements. Such networks might be implemented in softwareand/or hardware and include multi-dimensional codes, massively andstrategically parallel computers, optical computers and quantumcomputers. Further, more simple learning and modeling structures couldbe used, benefitting from future advances in computational speeds tocompensate for less sophistication in the learning and object detectionalgorithms. In such scenarios, the need for a CNN decreases, since at asufficiently high computation rate, “normal” fully connectedmulti-layered neural networks would become fast enough to providepractical learning and detector performance.

Images can be acquired by the camera 102 in a number of ways, dependingon what type of imaging is convenient for the implementation consideringfactors such as, but not limited to, working fluid, materials, andenvironmental factors. Image capture can be accomplished byelectromagnetic radiation in the: i) visible spectrum; ii) infraredspectrum; or iii) any electromagnetic radiation spectrum from which asuitable camera 102 can acquire images characterizing the multi-phasefluid flow phenomena. Images may also be indirectly acquired via meansother than by directly utilizing the electromagnetic radiation spectrum.Such other methods may include ultrasonic imaging. Images may also becollected and utilized differently during network training than whenutilized for real-time processing. Examples include the use ofhigh-quality video images paired with low-resolution images of the samesubject flow, to train the CNN to infer or reconstruct the high-qualityimages from the low-quality images. The low-quality image collection maybe more environmentally robust for use in the production process formonitoring and control in real-time. Images may also be acquired usinggimballed cameras or cameras associated with gimballed mirrors. Imagesmay also include source materials that are the reconstruction ofnon-visual or partially visible information, including derivations from,but not limited to, sound, vibrations, and odor.

If the images utilize a visible spectrum, methods for acquiring therequired images may include: i) augmenting the flow loop to include atransparent view port 208 in FIG. 2, to allow imaging by an externalcamera 102; ii) using optical devices embedded in the flow channels; oriii) using small cameras embedded in the flow channels. Images may alsobe acquired from stored previously recorded media, including video,photos, drawings, graphs, and animations. Such acquired images canaugment images collected from the external camera 102 or be utilizedinstead of images collected from an external camera 102.

The pre-processor 104 and post-processor 108 may be implemented insoftware and/or hardware, where such implementations may include fixedor adaptive code or neural network algorithms. The pre-processor 104 andpost-processor 108 provide the necessary communications protocol, datamanipulation and regulation, data correcting methods, physics-basedmodels, probabilistic models, and other processes and models required tocreate the input to the processor 106 and apply and distribute thecomputational reasoning based on the output of the processor 106,respectively.

Computational reasoning provided by the pre-processor 104 and thepost-processor 108 includes, but is not limited to, physics-basedmodeling, probabilistic modeling, fault and anomaly detectionalgorithms, best-estimate predictions, state estimations, adaptiveestimations, and mathematics and logic-based conclusions and outcomes.

Inputs to the pre-processor 104 and post-processor 108 include imagesource data of the multi-phase flow field and processor 106 output,respectively. The pre-processor 104 and post-processor 108 may alsoreceive auxiliary inputs including, but not limited to, constants anddirect measurements and control inputs related to the monitored orcontrolled processes and relevant environments, including but notlimited to, physical, mechanical, electro-mechanical, gravitational, andhuman behaviors, states, and conditions.

A communication link connects the camera 102, the pre-processor 104, theprocessor 106, the post-processor 108, and the controller 202. Thecommunication link may include direct connecting cables or wirelesscommunications, where wireless communications can include, but are notlimited to, electromagnetic radiation and optical means. The controller202, may be centralized or distributed, providing signals andinstructions to actuators and other active components of the flow loopimplementation.

A multi-phase flow path 210 may be any flow path, including, but notlimited to: i) channel flow; ii) planar flow; iii) spheroidal flow; iv)a porous media flow, or v) free trajectory flow (i.e. flowing in someregion that is not defined by walls or other mechanical boundary).Further, the multi-phase flow path 210 may be a closed-loop asillustrated in FIG. 2 or a once-through open loop, where the flow is notcontinually recycled, but travels over a finite path through a criticalvolume as illustrated in FIGS. 3-4. In FIG. 4, a flow exhaust to theenvironment can constitute the critical volume where the unboundedexhaust flow is imaged by the camera 102 or other imaging device. InFIG. 3 a display monitor receives output from the post-processor.Flow-loops may also be any combination of closed- and open-loop paths.

An example of an open loop flow path includes, but is not limited to,the rocket nozzle exhaust 402 in FIG. 4, which is an unboundedfree-trajectory flow. In the case of rocket nozzle exhaust 402, thecomposition of the exhaust can provide diagnostic information pertainingto incipient failure conditions concerning the rocket propulsion system.For high-temperature applications, the camera 102 may be protected by atransparent heat shield 404.

The active systems 204 may include pumps, valves, separator, rotators,sliders, and propulsion devices. The working fluid may be composed offluids, fluids and solids, or only solids. The heat exchanger 206illustrated in FIGS. 2-3 is an example of a critical volume, i.e. thevolume in the flow loop that is directly photographed by the high-speedcamera 102. Such critical volume may be a vital component of themonitored processes, or a special channel for imaging, or an exhauststream, or other critical volume. Augmentations of the flow loop asrequired to support imaging of the flow by the sensor system 100 mayinclude a view port 208 that is sufficiently transparent to theradiation spectrum and methods utilized for image capture.

Because the intelligent sensor system 100 relies on direct inspection ofmulti-phase phenomena and behaviors by machine vision, it can beconstructed to be robust in view of electromagnetic and radiationinterference. Further, machine-vision enables accurate and reliableinspection of multi-phase fluid flow, including those that areephemeral, which may be used to provide instructions to monitoring andcontrolling systems. The arbitrarily high number of types of multi-phasefluid flow phenomena that can be identified and inferred by theintelligent sensor system 100 renders it particularly useful in a broadnumber of industrial applications. Accordingly, the intelligent sensorsystem 100 provides for enhanced practical implementations ofmulti-phase fluid flow systems across many applications in manyindustries (e.g. oil and gas, space, manufacturing).

While the present disclosure has been described in connection withpresently preferred embodiments, it will be understood by those skilledin the art that it is not intended to limit the disclosure to thoseembodiments. It is therefore, contemplated that various alternativeembodiments and modifications may be made to the disclosed embodimentswithout departing from the spirit and scope of the disclosure defined bythe appended claims and equivalents thereof.

1. A system for monitoring a multi-phase fluid flow, comprising: apreprocessor for receiving, filtering, and formatting digital images ofthe multi-phase fluid flow; a processor that includes a neural networklearning algorithm linked to the preprocessor for receiving thepreprocessed digital images, identifying phenomena related to themulti-phase fluid flow in the preprocessed digital images andquantifying the relative positions and geometries of the phenomena basedon the digital images; and a post processor linked to the processor forreceiving the identified phenomena, receiving the quantified relativepositions and geometries of the phenomena, deriving dynamic attributesof the identified phenomena and inferring other attributes related tothe multi-phase fluid flow.
 2. The system of claim 1, further comprisinga camera linked to the preprocessor for capturing the digital images ofthe multi-phase fluid flow in real-time and transmitting the digitalimages to the preprocessor.
 3. The system of claim 1, further comprisingat least one of a pump, a valve, a separator, a rotator, a slider, and apropulsion device linked to the controller for controlling themulti-phase fluid flow.
 4. The system of claim 3, further comprising aheat exchanger linked to one of the pump, the valve, the separator, therotator, the slider, and the propulsion device for receiving themulti-phase fluid flow.
 5. The system of claim 2, wherein the camera isadapted to capture digital images by electromagnetic radiation in atleast one of a visible spectrum, an infrared spectrum, and anelectromagnetic radiation spectrum.
 6. The system of claim 2, whereinthe camera is adapted to capture digital images by ultrasonic imaging.7. The system of claim 1, wherein the multi-phase fluid flow comprisesone of a channel flow, a planar flow, a spheroidal flow, a porous-mediaflow, and a free trajectory flow.
 8. The system of claim 1, furthercomprising a controller for controlling the multi-phase fluid flow basedon at least one of the identified phenomena, the quantified relativepositions and geometries of the phenomena, the derived dynamicattributes of the phenomena, and the inferred other attributes.
 9. Thesystem of claim 1, further comprising a controller for controlling asystem that is dependent on the multi-phase fluid flow based on at leastone of the identified phenomena, the quantified relative positions andgeometries of the phenomena, the derived dynamic attributes of thephenomena, and the inferred other attributes.
 10. A method formonitoring a multi-phase fluid flow, comprising: capturing at least oneattribute of the multi-phase fluid flow; identifying phenomena relatedto the multi-phase fluid flow based on the at least one attribute of themulti-phase fluid flow; quantifying the relative positions andgeometries of the phenomena; and deriving dynamic attributes related tothe phenomena by correlating relative positions and geometries of thephenomena based on a sequence of the at least one attribute capturedover a predetermined time-period; inferring other attributes related tothe multi-phase fluid flow based on the identified phenomena, thequantified relative positions and geometries of the phenomena and thederived dynamic attributes related to the phenomena.
 11. The method ofclaim 10, further comprising controlling the multi-phase fluid flowbased on at least one of the identified phenomena, the quantifiedrelative positions and geometries of the phenomena and the deriveddynamic attributes related to the phenomena.
 12. The method of claim 10,further comprising controlling a system dependent on the multi-phasefluid flow based on at least one of the identified phenomena, thequantified relative positions and geometries of the phenomena and thederived dynamic attributes related to the phenomena.
 13. The method ofclaim 11, wherein the multi-phase fluid flow is controlled by acontroller linked to at least one of a pump, a valve, a separator, arotator, a slider, and a propulsion device.
 14. The method of claim 10,wherein the multi-phase fluid flow comprises one of a channel flow, aplanar flow, a spheroidal flow, a porous-media flow, and a freetrajectory flow.
 15. The method of claim 10, wherein the phenomenaincludes at least one of a void, a wave, a shadow, a dimple, a wrinkle,foam, a bubble, a particulate, a velocity, an acceleration, a materialdeposition rate, an erosion rate, a phasic critical behavior pointrelated to heat transfer, a fluid dynamic property, a thermodynamicproperty, a thermophysical property, an optical property, a physicalproperty, and a volumetric and mass flow rate.
 16. The method of claim10, wherein the at least one attribute includes at least one of adigital image, sound and odor.
 17. The method of claim 10, wherein thequantified relative positions and geometries of the phenomena aredetermined by superimposing polygons around the phenomena identifiedwith vertices assigned from a coordinate system.
 18. The method of claim10, wherein the quantified relative positions and geometries of thephenomena are determined by utilizing image segmentation methods. 19.The method of claim 10, wherein the phenomena are identified andcorrelated with the quantified relative positions and geometries of thephenomena by a processor that includes a neural network learningalgorithm.
 20. The method of claim 19, wherein the processor comprises aconvolutional neural network with an object detection algorithm.
 21. Themethod of claim 10, wherein the at least one attribute may be capturedby at least one of a video camera, a still photography camera, and aprerecorded media.